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Explore the future of MLOps tools and their adaptation to responsible and ethical AI in this 39-minute conference talk from MLOps World: Machine Learning in Production. Dive into the ethical guardrails every MLOps solution should implement to prepare for upcoming regulatory requirements and GDPR compliance. Learn about the 7 key principles of GDPR, core components of compliance, and the definition of personal data according to the regulation. Discover what's currently missing in MLOps systems and examine existing solutions. Understand the 4 pillars of privacy-preserving AI, including privacy concerns in training data, differential privacy, disclosure risk vs. data utility, redaction, de-identification, and synthetic data. Explore the potential of federated learning and gain insights into the EU Draft AI Regulation. Stay ahead of the curve in responsible AI implementation with guidance from Patricia Thaine, Co-Founder & CEO of Private AI and Computer Science PhD Candidate at the University of Toronto.
